COVID-19 identification in chest X-ray images using intelligent multi-level classification scenario

Babukarthik, R. G.; Dhasarathan, Chandramohan; Tripathi, Diwakar; Kumar, Manish; Sambasivam, G.

doi:10.1016/j.compeleceng.2022.108405

Cited by 8 publications

(3 citation statements)

References 26 publications

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“…Dynamic global communication [20] proposed an intelligent grid for effective data processing and transmission to collect and analyze the power grid [17] , [18] , [19] . Genetic Deep Learning Convolutional Neural Network (GDCNN) [21] , [22] designed an approach to predict COVID-19 with a partial swam intelligent optimization model and huddle particle swarm optimization. Numerous techniques and methodologies were proposed in the literature study for personal data prevention ( [23] ; Mhayuddin et al., 2020; [24] ).…”

Section: Literature Studymentioning

confidence: 99%

COVID-19 health data analysis and personal data preserving: A homomorphic privacy enforcement approach

Dhasarathan

Hasan

Islam

et al. 2023

Computer Communications

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Section: Literature Studymentioning

confidence: 99%

COVID-19 health data analysis and personal data preserving: A homomorphic privacy enforcement approach

Dhasarathan

Hasan

Islam

et al. 2023

Computer Communications

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“…The authors also checked the performance of deep learning algorithms, where they achieved an accuracy level of 84.32% for three-class stress classification. Furthermore, it is worth mentioning that novel deep learning techniques, such as genetic deep learning convolutional neural networks (GDCNNs) [38], [39], have appeared as a powerful tool for two-dimensional data classification tasks. To apply GDCNN to 1D data, however, comprehensive modifications or adaptations are required and such a topic is beyond the scope of this paper.…”

Section: Related Workmentioning

confidence: 99%

Multi-Class Stress Detection Through Heart Rate Variability: A Deep Neural Network Based Study

Mortensen,

Mollov,

Chatterjee

et al. 2023

IEEE Access

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Stress is a natural human reaction to demands or pressure, usually when perceived as harmful or/and toxic. When stress becomes constantly overwhelmed and prolonged, it increases the risk of mental health and physiological uneasiness. Furthermore, chronic stress raises the likelihood of mental health plagues such as anxiety, depression, and sleep disorder. Although measuring stress using physiological parameters such as heart rate variability (HRV) is a common approach, how to achieve ultra-high accuracy based on HRV measurements remains as a challenging task. HRV is not equivalent to heart rate. While heart rate is the average value of heartbeats per minute, HRV represents the variation of the time interval between successive heartbeats. The HRV measurements are related to the variance of RR intervals which stand for the time between successive R peaks. In this study, we investigate the role of HRV features as stress detection bio-markers and develop a machine learning-based model for multi-class stress detection. More specifically, a convolution neural network (CNN) based model is developed to detect multi-class stress, namely, no stress, interruption stress, and time pressure stress, based on both time-and frequency-domain features of HRV. Validated through a publicly available dataset, SWELL−KW, the achieved accuracy score of our model has reached 99.9% (Precision = 1, Recall = 1, F1−score = 1, and MCC = 0.99), thus outperforming the existing methods in the literature. In addition, this study demonstrates the effectiveness of essential HRV features for stress detection using a feature extraction technique, i.e., analysis of variance.

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“…In threecategory (COVID-19, normal, and viral pneumonia) and four-category (COVID-19, normal, lung opacity, and viral pneumonia) classification issues, their model outperformed the pre-trained model with an accuracy of 99.66% and 96.49%, respectively. Additionally, Babukarthik et al [33] built a GDCNN model to predict and classify various pneumonia illnesses using CXR images. In their proposed method, they utilized Huddle Particle Swarm Optimization to reach a classification accuracy of 97.23% for a range of pneumonia disorders, including COVID-19.…”

Section: Related Workmentioning

confidence: 99%

A Real Time Method for Distinguishing COVID-19 Utilizing 2D-CNN and Transfer Learning

Sultana

Nahiduzzaman

Bakchy

et al. 2023

Sensors

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Rapid identification of COVID-19 can assist in making decisions for effective treatment and epidemic prevention. The PCR-based test is expert-dependent, is time-consuming, and has limited sensitivity. By inspecting Chest R-ray (CXR) images, COVID-19, pneumonia, and other lung infections can be detected in real time. The current, state-of-the-art literature suggests that deep learning (DL) is highly advantageous in automatic disease classification utilizing the CXR images. The goal of this study is to develop models by employing DL models for identifying COVID-19 and other lung disorders more efficiently. For this study, a dataset of 18,564 CXR images with seven disease categories was created from multiple publicly available sources. Four DL architectures including the proposed CNN model and pretrained VGG-16, VGG-19, and Inception-v3 models were applied to identify healthy and six lung diseases (fibrosis, lung opacity, viral pneumonia, bacterial pneumonia, COVID-19, and tuberculosis). Accuracy, precision, recall, f1 score, area under the curve (AUC), and testing time were used to evaluate the performance of these four models. The results demonstrated that the proposed CNN model outperformed all other DL models employed for a seven-class classification with an accuracy of 93.15% and average values for precision, recall, f1-score, and AUC of 0.9343, 0.9443, 0.9386, and 0.9939. The CNN model equally performed well when other multiclass classifications including normal and COVID-19 as the common classes were considered, yielding accuracy values of 98%, 97.49%, 97.81%, 96%, and 96.75% for two, three, four, five, and six classes, respectively. The proposed model can also identify COVID-19 with shorter training and testing times compared to other transfer learning models.

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COVID-19 identification in chest X-ray images using intelligent multi-level classification scenario

Cited by 8 publications

References 26 publications

COVID-19 health data analysis and personal data preserving: A homomorphic privacy enforcement approach

COVID-19 health data analysis and personal data preserving: A homomorphic privacy enforcement approach

Multi-Class Stress Detection Through Heart Rate Variability: A Deep Neural Network Based Study

A Real Time Method for Distinguishing COVID-19 Utilizing 2D-CNN and Transfer Learning

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